rugarch gives two different results based on the same model…how is that even possible?

On Aug 19, 2018, at 5:46 PM, GALIB KHAN <ghk18 at scarletmail.rutgers.edu> wrote:

Recently I have discovered a problem with a package called rugarch that
creates arma-garch models. The issue is that if you literally change the
positions of the x variables (external regressors) then you get two
completely different results.

In other words:

  - model1 = (arma(2,2) + garch(1,0) + x1 + x2)
  - model2 = (arma(2,2) + garch(1,0) + x2 + x1)
  - rugarch's output is essentially saying that model1 != model2
  - When the correct result should be model1 == model2

I may not know a lot of statistics but I know for a fact that if you move
the x variables around, the output should still be the same.

Am I wrong on this?

Here's my stack exchange post that shows a generic R script proving my
point: Should the positioning of the external regressors change the output
of arma-garch? (Possible rugarch bug/error)
<https://stackoverflow.com/questions/51900177/should-the-positioning-of-the-external-regressors-change-the-output-of-arma-garc>

Any feedback is welcomed.

Thanks

	[[alternative HTML version deleted]]

I run the code you provided and obtain the following results related to
the external parameters:


Case 1 (x1,x2)
# x2 is second

            Estimate   Std. Error       t value  Pr(>|t|)
mxreg1  1.6724148 1.203377e-01  1.389767e+01 0.0000000
mxreg2  2.5310286 1.878833e-02  1.347128e+02 0.0000000

Case 2 (x2,x1)
# i.e. x2 is now first

mxreg1  2.5225382  0.04292725  58.7631024 0.000000e+00
mxreg2  1.6782986  0.12769622  13.1428990 0.000000e+00

Small differences in the coefficients are the result of the optimizer.
There may be an issues in the
way starting parameters are being generated based on some recent input
from Josh Ulrich (still to investigate)
and related to arima0 (used to generate start parameters), but otherwise
don?t see a large problem at first glance.

Alexios

On Aug 19, 2018, at 5:46 PM, GALIB KHAN <ghk18 at scarletmail.rutgers.edu>

wrote:

Recently I have discovered a problem with a package called rugarch that
creates arma-garch models. The issue is that if you literally change the
positions of the x variables (external regressors) then you get two
completely different results.

In other words:

  - model1 = (arma(2,2) + garch(1,0) + x1 + x2)
  - model2 = (arma(2,2) + garch(1,0) + x2 + x1)
  - rugarch's output is essentially saying that model1 != model2
  - When the correct result should be model1 == model2

I may not know a lot of statistics but I know for a fact that if you move
the x variables around, the output should still be the same.

Am I wrong on this?

Here's my stack exchange post that shows a generic R script proving my
point: Should the positioning of the external regressors change the

output

of arma-garch? (Possible rugarch bug/error)
<https://stackoverflow.com/questions/51900177/should-the-

positioning-of-the-external-regressors-change-the-output-of-arma-garc>

Any feedback is welcomed.

Thanks

      [[alternative HTML version deleted]]

should go.

On Aug 19, 2018, at 9:02 PM, GALIB KHAN <ghk18 at scarletmail.rutgers.edu> wrote:

Sorry for sending this again, I didn't include r-sig-finance in the email address. I'm still adjusting in how to respond.
 
Alexios,

Did you set the set the seed to 1, because I'm looking at your results and the numbers do not match with the numbers that I have provided. 

I understand why the coefficients' estimates are similar but it doesn't explain why other columns such as the t-value and pr are off by a large margin. Also estimates for mu, ar*, ma*, omega, alpha1, and shape may have large differences. 

Take mu as an example:
-7.538187e+00 - (-7.877120e+00) = 0.338933, isn't that considered a large difference to the point where it's safe to say that these two values are not similar?

Another example is the t-values for x1 and x2: 
x1 = 8.799994e+01   -  5.509361e+02 = -462.9362
x2 = 8.508606e+01   -  5.287634e+02 = -443.6773

An more alarming case that unfortunately I cannot share due to the data being sensitive is that when the x variables' positions are switched, the p-values are not the same. The p-value for a particular external regressor went from 0 to 0.4385.

I will attempt to re-create a separate generic dataset that is similar to the sensitive data that I am using. 


Galib Khan


On Sun, Aug 19, 2018 at 10:06 PM, alexios galanos <alexios at 4dscape.com> wrote:
I run the code you provided and obtain the following results related to the external parameters:


Case 1 (x1,x2)
# x2 is second

            Estimate   Std. Error       t value  Pr(>|t|)
mxreg1  1.6724148 1.203377e-01  1.389767e+01 0.0000000
mxreg2  2.5310286 1.878833e-02  1.347128e+02 0.0000000

Case 2 (x2,x1)
# i.e. x2 is now first

mxreg1  2.5225382  0.04292725  58.7631024 0.000000e+00
mxreg2  1.6782986  0.12769622  13.1428990 0.000000e+00

Small differences in the coefficients are the result of the optimizer. There may be an issues in the
way starting parameters are being generated based on some recent input from Josh Ulrich (still to investigate) 
and related to arima0 (used to generate start parameters), but otherwise don?t see a large problem at first glance.

Alexios

On Aug 19, 2018, at 5:46 PM, GALIB KHAN <ghk18 at scarletmail.rutgers.edu> wrote:

Recently I have discovered a problem with a package called rugarch that
creates arma-garch models. The issue is that if you literally change the
positions of the x variables (external regressors) then you get two
completely different results.

In other words:

  - model1 = (arma(2,2) + garch(1,0) + x1 + x2)
  - model2 = (arma(2,2) + garch(1,0) + x2 + x1)
  - rugarch's output is essentially saying that model1 != model2
  - When the correct result should be model1 == model2

I may not know a lot of statistics but I know for a fact that if you move
the x variables around, the output should still be the same.

Am I wrong on this?

Here's my stack exchange post that shows a generic R script proving my
point: Should the positioning of the external regressors change the output
of arma-garch? (Possible rugarch bug/error)
<https://stackoverflow.com/questions/51900177/should-the-positioning-of-the-external-regressors-change-the-output-of-arma-garc>

Any feedback is welcomed.

Thanks

      [[alternative HTML version deleted]]

I did use the seed you provided.

Use the following code for estimation:

fit <- ugarchfit(spec = spec, data = as.matrix(temp$y),solver = "nlminb",
fit.control=list(scale=1))

model_maker(var1)
         Estimate  Std. Error     t value     Pr(>|t|)
mu     -7.3998577  0.69086641 -10.7109821 0.0000000000
ar1     0.3387323  0.08280162   4.0908900 0.0000429721
ar2    -0.8834201  0.06569477 -13.4473414 0.0000000000
ma1    -0.2902069  0.08598589  -3.3750525 0.0007380161
ma2     0.8660807  0.06778418  12.7770320 0.0000000000
mxreg1  1.6782992  0.12769644  13.1428825 0.0000000000
mxreg2  2.5225382  0.04292728  58.7630625 0.0000000000
omega  12.0047145  0.82986864  14.4658010 0.0000000000
alpha1  0.0000000  0.07358520   0.0000000 1.0000000000
shape  63.0103309 98.49188643   0.6397515 0.5223341761

model_maker(var2)
         Estimate  Std. Error     t value     Pr(>|t|)
mu     -7.3998549  0.69086651 -10.7109764 0.000000e+00
ar1     0.3387334  0.08280150   4.0909088 4.296861e-05
ar2    -0.8834206  0.06569433 -13.4474406 0.000000e+00
ma1    -0.2902081  0.08598562  -3.3750776 7.379487e-04
ma2     0.8660811  0.06778412  12.7770487 0.000000e+00
mxreg1  2.5225383  0.04292728  58.7630642 0.000000e+00
mxreg2  1.6782987  0.12769640  13.1428817 0.000000e+00
omega  12.0047142  0.82992363  14.4648419 0.000000e+00
alpha1  0.0000000  0.07359329   0.0000000 1.000000e+00
shape  63.0105962 98.49368444   0.6397425 5.223400e-01


I can?t see any ?significant? differences, can you?
It?s completely related to the optimization/starting parameters. The
?scale? is documented and not on by default (perhaps it should be).

Alexios

On Aug 19, 2018, at 9:02 PM, GALIB KHAN <ghk18 at scarletmail.rutgers.edu>

wrote:

Sorry for sending this again, I didn't include r-sig-finance in the

email address. I'm still adjusting in how to respond.

Alexios,

Did you set the set the seed to 1, because I'm looking at your results

and the numbers do not match with the numbers that I have provided.

I understand why the coefficients' estimates are similar but it doesn't

explain why other columns such as the t-value and pr are off by a large
margin. Also estimates for mu, ar*, ma*, omega, alpha1, and shape may have
large differences.

Take mu as an example:
-7.538187e+00 - (-7.877120e+00) = 0.338933, isn't that considered a

large difference to the point where it's safe to say that these two values
are not similar?

Another example is the t-values for x1 and x2:
x1 = 8.799994e+01   -  5.509361e+02 = -462.9362
x2 = 8.508606e+01   -  5.287634e+02 = -443.6773

An more alarming case that unfortunately I cannot share due to the data

being sensitive is that when the x variables' positions are switched, the
p-values are not the same. The p-value for a particular external regressor
went from 0 to 0.4385.

I will attempt to re-create a separate generic dataset that is similar

to the sensitive data that I am using.

Galib Khan


On Sun, Aug 19, 2018 at 10:06 PM, alexios galanos <alexios at 4dscape.com>

wrote:

I run the code you provided and obtain the following results related to

the external parameters:

Case 1 (x1,x2)
# x2 is second

            Estimate   Std. Error       t value  Pr(>|t|)
mxreg1  1.6724148 1.203377e-01  1.389767e+01 0.0000000
mxreg2  2.5310286 1.878833e-02  1.347128e+02 0.0000000

Case 2 (x2,x1)
# i.e. x2 is now first

mxreg1  2.5225382  0.04292725  58.7631024 0.000000e+00
mxreg2  1.6782986  0.12769622  13.1428990 0.000000e+00

Small differences in the coefficients are the result of the optimizer.

There may be an issues in the

way starting parameters are being generated based on some recent input

from Josh Ulrich (still to investigate)

and related to arima0 (used to generate start parameters), but otherwise

don?t see a large problem at first glance.

Alexios

On Aug 19, 2018, at 5:46 PM, GALIB KHAN <ghk18 at scarletmail.rutgers.edu>

wrote:

Recently I have discovered a problem with a package called rugarch that
creates arma-garch models. The issue is that if you literally change

the

positions of the x variables (external regressors) then you get two
completely different results.

In other words:

  - model1 = (arma(2,2) + garch(1,0) + x1 + x2)
  - model2 = (arma(2,2) + garch(1,0) + x2 + x1)
  - rugarch's output is essentially saying that model1 != model2
  - When the correct result should be model1 == model2

I may not know a lot of statistics but I know for a fact that if you

move

the x variables around, the output should still be the same.

Am I wrong on this?

Here's my stack exchange post that shows a generic R script proving my
point: Should the positioning of the external regressors change the

output

of arma-garch? (Possible rugarch bug/error)
<https://stackoverflow.com/questions/51900177/should-the-

positioning-of-the-external-regressors-change-the-output-of-arma-garc>

Any feedback is welcomed.

Thanks

      [[alternative HTML version deleted]]

questions should go.

Alexios,


Veryyyy interesting!!!!! No I cannot see any differences at all lol.

I updated the code and indeed you are correct sir. Thank you for your time
in investigating this.

I will update my stack exchange post to reflect your answer in the morning.

Again thank you for all your help!!!!

Best,
Galib Khan


On Sun, Aug 19, 2018 at 11:19 PM, alexios galanos <alexios at 4dscape.com>
wrote:

I did use the seed you provided.

Use the following code for estimation:

fit <- ugarchfit(spec = spec, data = as.matrix(temp$y),solver = "nlminb",
fit.control=list(scale=1))

model_maker(var1)
         Estimate  Std. Error     t value     Pr(>|t|)
mu     -7.3998577  0.69086641 -10.7109821 0.0000000000
ar1     0.3387323  0.08280162   4.0908900 0.0000429721
ar2    -0.8834201  0.06569477 -13.4473414 0.0000000000
ma1    -0.2902069  0.08598589  -3.3750525 0.0007380161
ma2     0.8660807  0.06778418  12.7770320 0.0000000000
mxreg1  1.6782992  0.12769644  13.1428825 0.0000000000
mxreg2  2.5225382  0.04292728  58.7630625 0.0000000000
omega  12.0047145  0.82986864  14.4658010 0.0000000000
alpha1  0.0000000  0.07358520   0.0000000 1.0000000000
shape  63.0103309 98.49188643   0.6397515 0.5223341761

model_maker(var2)
         Estimate  Std. Error     t value     Pr(>|t|)
mu     -7.3998549  0.69086651 -10.7109764 0.000000e+00
ar1     0.3387334  0.08280150   4.0909088 4.296861e-05
ar2    -0.8834206  0.06569433 -13.4474406 0.000000e+00
ma1    -0.2902081  0.08598562  -3.3750776 7.379487e-04
ma2     0.8660811  0.06778412  12.7770487 0.000000e+00
mxreg1  2.5225383  0.04292728  58.7630642 0.000000e+00
mxreg2  1.6782987  0.12769640  13.1428817 0.000000e+00
omega  12.0047142  0.82992363  14.4648419 0.000000e+00
alpha1  0.0000000  0.07359329   0.0000000 1.000000e+00
shape  63.0105962 98.49368444   0.6397425 5.223400e-01


I can?t see any ?significant? differences, can you?
It?s completely related to the optimization/starting parameters. The
?scale? is documented and not on by default (perhaps it should be).

Alexios

On Aug 19, 2018, at 9:02 PM, GALIB KHAN <ghk18 at scarletmail.rutgers.edu>

wrote:

Sorry for sending this again, I didn't include r-sig-finance in the

email address. I'm still adjusting in how to respond.

Alexios,

Did you set the set the seed to 1, because I'm looking at your results

and the numbers do not match with the numbers that I have provided.

I understand why the coefficients' estimates are similar but it doesn't

explain why other columns such as the t-value and pr are off by a large
margin. Also estimates for mu, ar*, ma*, omega, alpha1, and shape may have
large differences.

Take mu as an example:
-7.538187e+00 - (-7.877120e+00) = 0.338933, isn't that considered a

large difference to the point where it's safe to say that these two values
are not similar?

Another example is the t-values for x1 and x2:
x1 = 8.799994e+01   -  5.509361e+02 = -462.9362
x2 = 8.508606e+01   -  5.287634e+02 = -443.6773

An more alarming case that unfortunately I cannot share due to the data

being sensitive is that when the x variables' positions are switched, the
p-values are not the same. The p-value for a particular external regressor
went from 0 to 0.4385.

I will attempt to re-create a separate generic dataset that is similar

to the sensitive data that I am using.

Galib Khan


On Sun, Aug 19, 2018 at 10:06 PM, alexios galanos <alexios at 4dscape.com>

wrote:

I run the code you provided and obtain the following results related to

the external parameters:

Case 1 (x1,x2)
# x2 is second

            Estimate   Std. Error       t value  Pr(>|t|)
mxreg1  1.6724148 1.203377e-01  1.389767e+01 0.0000000
mxreg2  2.5310286 1.878833e-02  1.347128e+02 0.0000000

Case 2 (x2,x1)
# i.e. x2 is now first

mxreg1  2.5225382  0.04292725  58.7631024 0.000000e+00
mxreg2  1.6782986  0.12769622  13.1428990 0.000000e+00

Small differences in the coefficients are the result of the optimizer.

There may be an issues in the

way starting parameters are being generated based on some recent input

from Josh Ulrich (still to investigate)

and related to arima0 (used to generate start parameters), but

otherwise don?t see a large problem at first glance.

Alexios

On Aug 19, 2018, at 5:46 PM, GALIB KHAN <

ghk18 at scarletmail.rutgers.edu> wrote:

Recently I have discovered a problem with a package called rugarch

that

creates arma-garch models. The issue is that if you literally change

the

positions of the x variables (external regressors) then you get two
completely different results.

In other words:

  - model1 = (arma(2,2) + garch(1,0) + x1 + x2)
  - model2 = (arma(2,2) + garch(1,0) + x2 + x1)
  - rugarch's output is essentially saying that model1 != model2
  - When the correct result should be model1 == model2

I may not know a lot of statistics but I know for a fact that if you

move

the x variables around, the output should still be the same.

Am I wrong on this?

Here's my stack exchange post that shows a generic R script proving my
point: Should the positioning of the external regressors change the

output

of arma-garch? (Possible rugarch bug/error)
<https://stackoverflow.com/questions/51900177/should-the-pos

itioning-of-the-external-regressors-change-the-output-of-arma-garc>

Any feedback is welcomed.

Thanks

      [[alternative HTML version deleted]]

questions should go.

Alexios,


Veryyyy interesting!!!!! No I cannot see any differences at all lol.

I updated the code and indeed you are correct sir. Thank you for your time
in investigating this.

I will update my stack exchange post to reflect your answer in the morning.

Again thank you for all your help!!!!

Best,
Galib Khan


On Sun, Aug 19, 2018 at 11:19 PM, alexios galanos <alexios at 4dscape.com>
wrote:

I did use the seed you provided.

Use the following code for estimation:

fit <- ugarchfit(spec = spec, data = as.matrix(temp$y),solver = "nlminb",
fit.control=list(scale=1))

model_maker(var1)
         Estimate  Std. Error     t value     Pr(>|t|)
mu     -7.3998577  0.69086641 -10.7109821 0.0000000000
ar1     0.3387323  0.08280162   4.0908900 0.0000429721
ar2    -0.8834201  0.06569477 -13.4473414 0.0000000000
ma1    -0.2902069  0.08598589  -3.3750525 0.0007380161
ma2     0.8660807  0.06778418  12.7770320 0.0000000000
mxreg1  1.6782992  0.12769644  13.1428825 0.0000000000
mxreg2  2.5225382  0.04292728  58.7630625 0.0000000000
omega  12.0047145  0.82986864  14.4658010 0.0000000000
alpha1  0.0000000  0.07358520   0.0000000 1.0000000000
shape  63.0103309 98.49188643   0.6397515 0.5223341761

model_maker(var2)
         Estimate  Std. Error     t value     Pr(>|t|)
mu     -7.3998549  0.69086651 -10.7109764 0.000000e+00
ar1     0.3387334  0.08280150   4.0909088 4.296861e-05
ar2    -0.8834206  0.06569433 -13.4474406 0.000000e+00
ma1    -0.2902081  0.08598562  -3.3750776 7.379487e-04
ma2     0.8660811  0.06778412  12.7770487 0.000000e+00
mxreg1  2.5225383  0.04292728  58.7630642 0.000000e+00
mxreg2  1.6782987  0.12769640  13.1428817 0.000000e+00
omega  12.0047142  0.82992363  14.4648419 0.000000e+00
alpha1  0.0000000  0.07359329   0.0000000 1.000000e+00
shape  63.0105962 98.49368444   0.6397425 5.223400e-01


I can?t see any ?significant? differences, can you?
It?s completely related to the optimization/starting parameters. The
?scale? is documented and not on by default (perhaps it should be).

Alexios

On Aug 19, 2018, at 9:02 PM, GALIB KHAN <ghk18 at scarletmail.rutgers.edu>

wrote:

Sorry for sending this again, I didn't include r-sig-finance in the

email address. I'm still adjusting in how to respond.

Alexios,

Did you set the set the seed to 1, because I'm looking at your results

and the numbers do not match with the numbers that I have provided.

I understand why the coefficients' estimates are similar but it doesn't

explain why other columns such as the t-value and pr are off by a large
margin. Also estimates for mu, ar*, ma*, omega, alpha1, and shape may have
large differences.

Take mu as an example:
-7.538187e+00 - (-7.877120e+00) = 0.338933, isn't that considered a

large difference to the point where it's safe to say that these two values
are not similar?

Another example is the t-values for x1 and x2:
x1 = 8.799994e+01   -  5.509361e+02 = -462.9362
x2 = 8.508606e+01   -  5.287634e+02 = -443.6773

An more alarming case that unfortunately I cannot share due to the data

being sensitive is that when the x variables' positions are switched, the
p-values are not the same. The p-value for a particular external regressor
went from 0 to 0.4385.

I will attempt to re-create a separate generic dataset that is similar

to the sensitive data that I am using.

Galib Khan


On Sun, Aug 19, 2018 at 10:06 PM, alexios galanos <alexios at 4dscape.com>

wrote:

I run the code you provided and obtain the following results related to

the external parameters:

Case 1 (x1,x2)
# x2 is second

            Estimate   Std. Error       t value  Pr(>|t|)
mxreg1  1.6724148 1.203377e-01  1.389767e+01 0.0000000
mxreg2  2.5310286 1.878833e-02  1.347128e+02 0.0000000

Case 2 (x2,x1)
# i.e. x2 is now first

mxreg1  2.5225382  0.04292725  58.7631024 0.000000e+00
mxreg2  1.6782986  0.12769622  13.1428990 0.000000e+00

Small differences in the coefficients are the result of the optimizer.

There may be an issues in the

way starting parameters are being generated based on some recent input

from Josh Ulrich (still to investigate)

and related to arima0 (used to generate start parameters), but

otherwise don?t see a large problem at first glance.

Alexios

On Aug 19, 2018, at 5:46 PM, GALIB KHAN <

ghk18 at scarletmail.rutgers.edu> wrote:

Recently I have discovered a problem with a package called rugarch

that

creates arma-garch models. The issue is that if you literally change

the

positions of the x variables (external regressors) then you get two
completely different results.

In other words:

  - model1 = (arma(2,2) + garch(1,0) + x1 + x2)
  - model2 = (arma(2,2) + garch(1,0) + x2 + x1)
  - rugarch's output is essentially saying that model1 != model2
  - When the correct result should be model1 == model2

I may not know a lot of statistics but I know for a fact that if you

move

the x variables around, the output should still be the same.

Am I wrong on this?

Here's my stack exchange post that shows a generic R script proving my
point: Should the positioning of the external regressors change the

output

of arma-garch? (Possible rugarch bug/error)
<https://stackoverflow.com/questions/51900177/should-the-pos

itioning-of-the-external-regressors-change-the-output-of-arma-garc>

Any feedback is welcomed.

Thanks

      [[alternative HTML version deleted]]

questions should go.

Alexios,

I changed the solver to "hybrid" but kept the scaling that you provided
and got different results when switching the positions for the external
regressors.

ugarchfit(spec = spec, data = as.matrix(temp$y),solver =
"hybrid",fit.control=list(scale=1))

So it looks like you have to change the solver to "nlminb" and update
fit.control with the scaling that you provided.

Galib


On Sun, Aug 19, 2018 at 11:41 PM, GALIB KHAN <
ghk18 at scarletmail.rutgers.edu> wrote:

Alexios,


Veryyyy interesting!!!!! No I cannot see any differences at all lol.

I updated the code and indeed you are correct sir. Thank you for your
time in investigating this.

I will update my stack exchange post to reflect your answer in the
morning.

Again thank you for all your help!!!!

Best,
Galib Khan


On Sun, Aug 19, 2018 at 11:19 PM, alexios galanos <alexios at 4dscape.com>
wrote:

I did use the seed you provided.

Use the following code for estimation:

fit <- ugarchfit(spec = spec, data = as.matrix(temp$y),solver =
"nlminb", fit.control=list(scale=1))

model_maker(var1)
         Estimate  Std. Error     t value     Pr(>|t|)
mu     -7.3998577  0.69086641 -10.7109821 0.0000000000
ar1     0.3387323  0.08280162   4.0908900 0.0000429721
ar2    -0.8834201  0.06569477 -13.4473414 0.0000000000
ma1    -0.2902069  0.08598589  -3.3750525 0.0007380161
ma2     0.8660807  0.06778418  12.7770320 0.0000000000
mxreg1  1.6782992  0.12769644  13.1428825 0.0000000000
mxreg2  2.5225382  0.04292728  58.7630625 0.0000000000
omega  12.0047145  0.82986864  14.4658010 0.0000000000
alpha1  0.0000000  0.07358520   0.0000000 1.0000000000
shape  63.0103309 98.49188643   0.6397515 0.5223341761

model_maker(var2)
         Estimate  Std. Error     t value     Pr(>|t|)
mu     -7.3998549  0.69086651 -10.7109764 0.000000e+00
ar1     0.3387334  0.08280150   4.0909088 4.296861e-05
ar2    -0.8834206  0.06569433 -13.4474406 0.000000e+00
ma1    -0.2902081  0.08598562  -3.3750776 7.379487e-04
ma2     0.8660811  0.06778412  12.7770487 0.000000e+00
mxreg1  2.5225383  0.04292728  58.7630642 0.000000e+00
mxreg2  1.6782987  0.12769640  13.1428817 0.000000e+00
omega  12.0047142  0.82992363  14.4648419 0.000000e+00
alpha1  0.0000000  0.07359329   0.0000000 1.000000e+00
shape  63.0105962 98.49368444   0.6397425 5.223400e-01


I can?t see any ?significant? differences, can you?
It?s completely related to the optimization/starting parameters. The
?scale? is documented and not on by default (perhaps it should be).

Alexios

On Aug 19, 2018, at 9:02 PM, GALIB KHAN <ghk18 at scarletmail.rutgers.edu>

wrote:

Sorry for sending this again, I didn't include r-sig-finance in the

email address. I'm still adjusting in how to respond.

Alexios,

Did you set the set the seed to 1, because I'm looking at your results

and the numbers do not match with the numbers that I have provided.

I understand why the coefficients' estimates are similar but it

doesn't explain why other columns such as the t-value and pr are off by a
large margin. Also estimates for mu, ar*, ma*, omega, alpha1, and shape may
have large differences.

Take mu as an example:
-7.538187e+00 - (-7.877120e+00) = 0.338933, isn't that considered a

large difference to the point where it's safe to say that these two values
are not similar?

Another example is the t-values for x1 and x2:
x1 = 8.799994e+01   -  5.509361e+02 = -462.9362
x2 = 8.508606e+01   -  5.287634e+02 = -443.6773

An more alarming case that unfortunately I cannot share due to the

data being sensitive is that when the x variables' positions are switched,
the p-values are not the same. The p-value for a particular external
regressor went from 0 to 0.4385.

I will attempt to re-create a separate generic dataset that is similar

to the sensitive data that I am using.

Galib Khan


On Sun, Aug 19, 2018 at 10:06 PM, alexios galanos <alexios at 4dscape.com>

wrote:

I run the code you provided and obtain the following results related

to the external parameters:

Case 1 (x1,x2)
# x2 is second

            Estimate   Std. Error       t value  Pr(>|t|)
mxreg1  1.6724148 1.203377e-01  1.389767e+01 0.0000000
mxreg2  2.5310286 1.878833e-02  1.347128e+02 0.0000000

Case 2 (x2,x1)
# i.e. x2 is now first

mxreg1  2.5225382  0.04292725  58.7631024 0.000000e+00
mxreg2  1.6782986  0.12769622  13.1428990 0.000000e+00

Small differences in the coefficients are the result of the optimizer.

There may be an issues in the

way starting parameters are being generated based on some recent input

from Josh Ulrich (still to investigate)

and related to arima0 (used to generate start parameters), but

otherwise don?t see a large problem at first glance.

Alexios

On Aug 19, 2018, at 5:46 PM, GALIB KHAN <

ghk18 at scarletmail.rutgers.edu> wrote:

Recently I have discovered a problem with a package called rugarch

that

creates arma-garch models. The issue is that if you literally change

the

positions of the x variables (external regressors) then you get two
completely different results.

In other words:

  - model1 = (arma(2,2) + garch(1,0) + x1 + x2)
  - model2 = (arma(2,2) + garch(1,0) + x2 + x1)
  - rugarch's output is essentially saying that model1 != model2
  - When the correct result should be model1 == model2

I may not know a lot of statistics but I know for a fact that if you

move

the x variables around, the output should still be the same.

Am I wrong on this?

Here's my stack exchange post that shows a generic R script proving

my

point: Should the positioning of the external regressors change the

output

of arma-garch? (Possible rugarch bug/error)
<

https://stackoverflow.com/questions/51900177/should-the-positioning-of-the-external-regressors-change-the-output-of-arma-garc

Any feedback is welcomed.

Thanks

      [[alternative HTML version deleted]]

questions should go.

Galib Khan


On Sun, Aug 19, 2018 at 11:41 PM, GALIB KHAN <
ghk18 at scarletmail.rutgers.edu> wrote:

Alexios,


Veryyyy interesting!!!!! No I cannot see any differences at all lol.

I updated the code and indeed you are correct sir. Thank you for your
time in investigating this.

I will update my stack exchange post to reflect your answer in the
morning.

Again thank you for all your help!!!!

Best,
Galib Khan


On Sun, Aug 19, 2018 at 11:19 PM, alexios galanos <alexios at 4dscape.com>
wrote:

I did use the seed you provided.

Use the following code for estimation:

fit <- ugarchfit(spec = spec, data = as.matrix(temp$y),solver =
"nlminb", fit.control=list(scale=1))

model_maker(var1)
         Estimate  Std. Error     t value     Pr(>|t|)
mu     -7.3998577  0.69086641 -10.7109821 0.0000000000
ar1     0.3387323  0.08280162   4.0908900 0.0000429721
ar2    -0.8834201  0.06569477 -13.4473414 0.0000000000
ma1    -0.2902069  0.08598589  -3.3750525 0.0007380161
ma2     0.8660807  0.06778418  12.7770320 0.0000000000
mxreg1  1.6782992  0.12769644  13.1428825 0.0000000000
mxreg2  2.5225382  0.04292728  58.7630625 0.0000000000
omega  12.0047145  0.82986864  14.4658010 0.0000000000
alpha1  0.0000000  0.07358520   0.0000000 1.0000000000
shape  63.0103309 98.49188643   0.6397515 0.5223341761

model_maker(var2)
         Estimate  Std. Error     t value     Pr(>|t|)
mu     -7.3998549  0.69086651 -10.7109764 0.000000e+00
ar1     0.3387334  0.08280150   4.0909088 4.296861e-05
ar2    -0.8834206  0.06569433 -13.4474406 0.000000e+00
ma1    -0.2902081  0.08598562  -3.3750776 7.379487e-04
ma2     0.8660811  0.06778412  12.7770487 0.000000e+00
mxreg1  2.5225383  0.04292728  58.7630642 0.000000e+00
mxreg2  1.6782987  0.12769640  13.1428817 0.000000e+00
omega  12.0047142  0.82992363  14.4648419 0.000000e+00
alpha1  0.0000000  0.07359329   0.0000000 1.000000e+00
shape  63.0105962 98.49368444   0.6397425 5.223400e-01


I can?t see any ?significant? differences, can you?
It?s completely related to the optimization/starting parameters. The
?scale? is documented and not on by default (perhaps it should be).

Alexios

On Aug 19, 2018, at 9:02 PM, GALIB KHAN <ghk18 at scarletmail.rutgers.edu>

wrote:

Sorry for sending this again, I didn't include r-sig-finance in the

email address. I'm still adjusting in how to respond.

Alexios,

Did you set the set the seed to 1, because I'm looking at your results

and the numbers do not match with the numbers that I have provided.

I understand why the coefficients' estimates are similar but it

doesn't explain why other columns such as the t-value and pr are off by a
large margin. Also estimates for mu, ar*, ma*, omega, alpha1, and shape may
have large differences.

Take mu as an example:
-7.538187e+00 - (-7.877120e+00) = 0.338933, isn't that considered a

large difference to the point where it's safe to say that these two values
are not similar?

Another example is the t-values for x1 and x2:
x1 = 8.799994e+01   -  5.509361e+02 = -462.9362
x2 = 8.508606e+01   -  5.287634e+02 = -443.6773

An more alarming case that unfortunately I cannot share due to the

data being sensitive is that when the x variables' positions are switched,
the p-values are not the same. The p-value for a particular external
regressor went from 0 to 0.4385.

I will attempt to re-create a separate generic dataset that is similar

to the sensitive data that I am using.

Galib Khan


On Sun, Aug 19, 2018 at 10:06 PM, alexios galanos <alexios at 4dscape.com>

wrote:

I run the code you provided and obtain the following results related

to the external parameters:

Case 1 (x1,x2)
# x2 is second

            Estimate   Std. Error       t value  Pr(>|t|)
mxreg1  1.6724148 1.203377e-01  1.389767e+01 0.0000000
mxreg2  2.5310286 1.878833e-02  1.347128e+02 0.0000000

Case 2 (x2,x1)
# i.e. x2 is now first

mxreg1  2.5225382  0.04292725  58.7631024 0.000000e+00
mxreg2  1.6782986  0.12769622  13.1428990 0.000000e+00

Small differences in the coefficients are the result of the optimizer.

There may be an issues in the

way starting parameters are being generated based on some recent input

from Josh Ulrich (still to investigate)

and related to arima0 (used to generate start parameters), but

otherwise don?t see a large problem at first glance.

Alexios

On Aug 19, 2018, at 5:46 PM, GALIB KHAN <

ghk18 at scarletmail.rutgers.edu> wrote:

Recently I have discovered a problem with a package called rugarch

that

creates arma-garch models. The issue is that if you literally change

the

positions of the x variables (external regressors) then you get two
completely different results.

In other words:

  - model1 = (arma(2,2) + garch(1,0) + x1 + x2)
  - model2 = (arma(2,2) + garch(1,0) + x2 + x1)
  - rugarch's output is essentially saying that model1 != model2
  - When the correct result should be model1 == model2

I may not know a lot of statistics but I know for a fact that if you

move

the x variables around, the output should still be the same.

Am I wrong on this?

Here's my stack exchange post that shows a generic R script proving

my

point: Should the positioning of the external regressors change the

output

of arma-garch? (Possible rugarch bug/error)
<

https://stackoverflow.com/questions/51900177/should-the-positioning-of-the-external-regressors-change-the-output-of-arma-garc

Any feedback is welcomed.

Thanks

      [[alternative HTML version deleted]]

questions should go.

Again sorry for the repeat. I accidentally created a new post when I
wanted to reply back.

Alexios,

I updated my script to use multiple solvers and compare results.

It looks like nlminb is the most accurate solver for this generic dataset
that I am using.

I posted the results on stack exchange so its easier to view the results.

If you want, I can post the results here.

Here is the url with the updated script and results:


https://stackoverflow.com/questions/51900177/should-the-positioning-of-the-external-regressors-change-the-output-of-arma-garc/

On Mon, Aug 20, 2018, 7:29 AM GALIB KHAN <ghk18 at scarletmail.rutgers.edu>
wrote:

Alexios,

I changed the solver to "hybrid" but kept the scaling that you provided
and got different results when switching the positions for the external
regressors.

ugarchfit(spec = spec, data = as.matrix(temp$y),solver =
"hybrid",fit.control=list(scale=1))

So it looks like you have to change the solver to "nlminb" and update
fit.control with the scaling that you provided.

Galib


On Sun, Aug 19, 2018 at 11:41 PM, GALIB KHAN <
ghk18 at scarletmail.rutgers.edu> wrote:

Alexios,


Veryyyy interesting!!!!! No I cannot see any differences at all lol.

I updated the code and indeed you are correct sir. Thank you for your
time in investigating this.

I will update my stack exchange post to reflect your answer in the
morning.

Again thank you for all your help!!!!

Best,
Galib Khan


On Sun, Aug 19, 2018 at 11:19 PM, alexios galanos <alexios at 4dscape.com>
wrote:

I did use the seed you provided.

Use the following code for estimation:

fit <- ugarchfit(spec = spec, data = as.matrix(temp$y),solver =
"nlminb", fit.control=list(scale=1))

model_maker(var1)
         Estimate  Std. Error     t value     Pr(>|t|)
mu     -7.3998577  0.69086641 -10.7109821 0.0000000000
ar1     0.3387323  0.08280162   4.0908900 0.0000429721
ar2    -0.8834201  0.06569477 -13.4473414 0.0000000000
ma1    -0.2902069  0.08598589  -3.3750525 0.0007380161
ma2     0.8660807  0.06778418  12.7770320 0.0000000000
mxreg1  1.6782992  0.12769644  13.1428825 0.0000000000
mxreg2  2.5225382  0.04292728  58.7630625 0.0000000000
omega  12.0047145  0.82986864  14.4658010 0.0000000000
alpha1  0.0000000  0.07358520   0.0000000 1.0000000000
shape  63.0103309 98.49188643   0.6397515 0.5223341761

model_maker(var2)
         Estimate  Std. Error     t value     Pr(>|t|)
mu     -7.3998549  0.69086651 -10.7109764 0.000000e+00
ar1     0.3387334  0.08280150   4.0909088 4.296861e-05
ar2    -0.8834206  0.06569433 -13.4474406 0.000000e+00
ma1    -0.2902081  0.08598562  -3.3750776 7.379487e-04
ma2     0.8660811  0.06778412  12.7770487 0.000000e+00
mxreg1  2.5225383  0.04292728  58.7630642 0.000000e+00
mxreg2  1.6782987  0.12769640  13.1428817 0.000000e+00
omega  12.0047142  0.82992363  14.4648419 0.000000e+00
alpha1  0.0000000  0.07359329   0.0000000 1.000000e+00
shape  63.0105962 98.49368444   0.6397425 5.223400e-01


I can?t see any ?significant? differences, can you?
It?s completely related to the optimization/starting parameters. The
?scale? is documented and not on by default (perhaps it should be).

Alexios

On Aug 19, 2018, at 9:02 PM, GALIB KHAN <

ghk18 at scarletmail.rutgers.edu> wrote:

Sorry for sending this again, I didn't include r-sig-finance in the

email address. I'm still adjusting in how to respond.

Alexios,

Did you set the set the seed to 1, because I'm looking at your

results and the numbers do not match with the numbers that I have provided.

I understand why the coefficients' estimates are similar but it

doesn't explain why other columns such as the t-value and pr are off by a
large margin. Also estimates for mu, ar*, ma*, omega, alpha1, and shape may
have large differences.

Take mu as an example:
-7.538187e+00 - (-7.877120e+00) = 0.338933, isn't that considered a

large difference to the point where it's safe to say that these two values
are not similar?

Another example is the t-values for x1 and x2:
x1 = 8.799994e+01   -  5.509361e+02 = -462.9362
x2 = 8.508606e+01   -  5.287634e+02 = -443.6773

An more alarming case that unfortunately I cannot share due to the

data being sensitive is that when the x variables' positions are switched,
the p-values are not the same. The p-value for a particular external
regressor went from 0 to 0.4385.

I will attempt to re-create a separate generic dataset that is

similar to the sensitive data that I am using.

Galib Khan


On Sun, Aug 19, 2018 at 10:06 PM, alexios galanos <

alexios at 4dscape.com> wrote:

I run the code you provided and obtain the following results related

to the external parameters:

Case 1 (x1,x2)
# x2 is second

            Estimate   Std. Error       t value  Pr(>|t|)
mxreg1  1.6724148 1.203377e-01  1.389767e+01 0.0000000
mxreg2  2.5310286 1.878833e-02  1.347128e+02 0.0000000

Case 2 (x2,x1)
# i.e. x2 is now first

mxreg1  2.5225382  0.04292725  58.7631024 0.000000e+00
mxreg2  1.6782986  0.12769622  13.1428990 0.000000e+00

Small differences in the coefficients are the result of the

optimizer. There may be an issues in the

way starting parameters are being generated based on some recent

input from Josh Ulrich (still to investigate)

and related to arima0 (used to generate start parameters), but

otherwise don?t see a large problem at first glance.

Alexios

On Aug 19, 2018, at 5:46 PM, GALIB KHAN <

ghk18 at scarletmail.rutgers.edu> wrote:

Recently I have discovered a problem with a package called rugarch

that

creates arma-garch models. The issue is that if you literally

change the

positions of the x variables (external regressors) then you get two
completely different results.

In other words:

  - model1 = (arma(2,2) + garch(1,0) + x1 + x2)
  - model2 = (arma(2,2) + garch(1,0) + x2 + x1)
  - rugarch's output is essentially saying that model1 != model2
  - When the correct result should be model1 == model2

I may not know a lot of statistics but I know for a fact that if

you move

the x variables around, the output should still be the same.

Am I wrong on this?

Here's my stack exchange post that shows a generic R script proving

my

point: Should the positioning of the external regressors change the

output

of arma-garch? (Possible rugarch bug/error)
<

https://stackoverflow.com/questions/51900177/should-the-positioning-of-the-external-regressors-change-the-output-of-arma-garc

Any feedback is welcomed.

Thanks

      [[alternative HTML version deleted]]

questions should go.

Galib Khan


On Sun, Aug 19, 2018 at 11:41 PM, GALIB KHAN <
ghk18 at scarletmail.rutgers.edu> wrote:

Alexios,


Veryyyy interesting!!!!! No I cannot see any differences at all lol.

I updated the code and indeed you are correct sir. Thank you for your
time in investigating this.

I will update my stack exchange post to reflect your answer in the
morning.

Again thank you for all your help!!!!

Best,
Galib Khan


On Sun, Aug 19, 2018 at 11:19 PM, alexios galanos <alexios at 4dscape.com>
wrote:

I did use the seed you provided.

Use the following code for estimation:

fit <- ugarchfit(spec = spec, data = as.matrix(temp$y),solver =
"nlminb", fit.control=list(scale=1))

model_maker(var1)
         Estimate  Std. Error     t value     Pr(>|t|)
mu     -7.3998577  0.69086641 -10.7109821 0.0000000000
ar1     0.3387323  0.08280162   4.0908900 0.0000429721
ar2    -0.8834201  0.06569477 -13.4473414 0.0000000000
ma1    -0.2902069  0.08598589  -3.3750525 0.0007380161
ma2     0.8660807  0.06778418  12.7770320 0.0000000000
mxreg1  1.6782992  0.12769644  13.1428825 0.0000000000
mxreg2  2.5225382  0.04292728  58.7630625 0.0000000000
omega  12.0047145  0.82986864  14.4658010 0.0000000000
alpha1  0.0000000  0.07358520   0.0000000 1.0000000000
shape  63.0103309 98.49188643   0.6397515 0.5223341761

model_maker(var2)
         Estimate  Std. Error     t value     Pr(>|t|)
mu     -7.3998549  0.69086651 -10.7109764 0.000000e+00
ar1     0.3387334  0.08280150   4.0909088 4.296861e-05
ar2    -0.8834206  0.06569433 -13.4474406 0.000000e+00
ma1    -0.2902081  0.08598562  -3.3750776 7.379487e-04
ma2     0.8660811  0.06778412  12.7770487 0.000000e+00
mxreg1  2.5225383  0.04292728  58.7630642 0.000000e+00
mxreg2  1.6782987  0.12769640  13.1428817 0.000000e+00
omega  12.0047142  0.82992363  14.4648419 0.000000e+00
alpha1  0.0000000  0.07359329   0.0000000 1.000000e+00
shape  63.0105962 98.49368444   0.6397425 5.223400e-01


I can?t see any ?significant? differences, can you?
It?s completely related to the optimization/starting parameters. The
?scale? is documented and not on by default (perhaps it should be).

Alexios

On Aug 19, 2018, at 9:02 PM, GALIB KHAN <

ghk18 at scarletmail.rutgers.edu> wrote:

Sorry for sending this again, I didn't include r-sig-finance in the

email address. I'm still adjusting in how to respond.

Alexios,

Did you set the set the seed to 1, because I'm looking at your

results and the numbers do not match with the numbers that I have provided.

I understand why the coefficients' estimates are similar but it

doesn't explain why other columns such as the t-value and pr are off by a
large margin. Also estimates for mu, ar*, ma*, omega, alpha1, and shape may
have large differences.

Take mu as an example:
-7.538187e+00 - (-7.877120e+00) = 0.338933, isn't that considered a

large difference to the point where it's safe to say that these two values
are not similar?

Another example is the t-values for x1 and x2:
x1 = 8.799994e+01   -  5.509361e+02 = -462.9362
x2 = 8.508606e+01   -  5.287634e+02 = -443.6773

An more alarming case that unfortunately I cannot share due to the

data being sensitive is that when the x variables' positions are switched,
the p-values are not the same. The p-value for a particular external
regressor went from 0 to 0.4385.

I will attempt to re-create a separate generic dataset that is

similar to the sensitive data that I am using.

Galib Khan


On Sun, Aug 19, 2018 at 10:06 PM, alexios galanos <

alexios at 4dscape.com> wrote:

I run the code you provided and obtain the following results related

to the external parameters:

Case 1 (x1,x2)
# x2 is second

            Estimate   Std. Error       t value  Pr(>|t|)
mxreg1  1.6724148 1.203377e-01  1.389767e+01 0.0000000
mxreg2  2.5310286 1.878833e-02  1.347128e+02 0.0000000

Case 2 (x2,x1)
# i.e. x2 is now first

mxreg1  2.5225382  0.04292725  58.7631024 0.000000e+00
mxreg2  1.6782986  0.12769622  13.1428990 0.000000e+00

Small differences in the coefficients are the result of the

optimizer. There may be an issues in the

way starting parameters are being generated based on some recent

input from Josh Ulrich (still to investigate)

and related to arima0 (used to generate start parameters), but

otherwise don?t see a large problem at first glance.

Alexios

On Aug 19, 2018, at 5:46 PM, GALIB KHAN <

ghk18 at scarletmail.rutgers.edu> wrote:

Recently I have discovered a problem with a package called rugarch

that

creates arma-garch models. The issue is that if you literally

change the

positions of the x variables (external regressors) then you get two
completely different results.

In other words:

  - model1 = (arma(2,2) + garch(1,0) + x1 + x2)
  - model2 = (arma(2,2) + garch(1,0) + x2 + x1)
  - rugarch's output is essentially saying that model1 != model2
  - When the correct result should be model1 == model2

I may not know a lot of statistics but I know for a fact that if

you move

the x variables around, the output should still be the same.

Am I wrong on this?

Here's my stack exchange post that shows a generic R script proving

my

point: Should the positioning of the external regressors change the

output

of arma-garch? (Possible rugarch bug/error)
<

https://stackoverflow.com/questions/51900177/should-the-positioning-of-the-external-regressors-change-the-output-of-arma-garc

Any feedback is welcomed.

Thanks

      [[alternative HTML version deleted]]

questions should go.

I did use the seed you provided.

Use the following code for estimation:

fit <- ugarchfit(spec = spec, data = as.matrix(temp$y),solver = "nlminb",
fit.control=list(scale=1))

model_maker(var1)
         Estimate  Std. Error     t value     Pr(>|t|)
mu     -7.3998577  0.69086641 -10.7109821 0.0000000000
ar1     0.3387323  0.08280162   4.0908900 0.0000429721
ar2    -0.8834201  0.06569477 -13.4473414 0.0000000000
ma1    -0.2902069  0.08598589  -3.3750525 0.0007380161
ma2     0.8660807  0.06778418  12.7770320 0.0000000000
mxreg1  1.6782992  0.12769644  13.1428825 0.0000000000
mxreg2  2.5225382  0.04292728  58.7630625 0.0000000000
omega  12.0047145  0.82986864  14.4658010 0.0000000000
alpha1  0.0000000  0.07358520   0.0000000 1.0000000000
shape  63.0103309 98.49188643   0.6397515 0.5223341761

model_maker(var2)
         Estimate  Std. Error     t value     Pr(>|t|)
mu     -7.3998549  0.69086651 -10.7109764 0.000000e+00
ar1     0.3387334  0.08280150   4.0909088 4.296861e-05
ar2    -0.8834206  0.06569433 -13.4474406 0.000000e+00
ma1    -0.2902081  0.08598562  -3.3750776 7.379487e-04
ma2     0.8660811  0.06778412  12.7770487 0.000000e+00
mxreg1  2.5225383  0.04292728  58.7630642 0.000000e+00
mxreg2  1.6782987  0.12769640  13.1428817 0.000000e+00
omega  12.0047142  0.82992363  14.4648419 0.000000e+00
alpha1  0.0000000  0.07359329   0.0000000 1.000000e+00
shape  63.0105962 98.49368444   0.6397425 5.223400e-01


I can?t see any ?significant? differences, can you?
It?s completely related to the optimization/starting parameters. The
?scale? is documented and not on by default (perhaps it should be).

Alexios

On Aug 19, 2018, at 9:02 PM, GALIB KHAN <ghk18 at scarletmail.rutgers.edu>

wrote:

Sorry for sending this again, I didn't include r-sig-finance in the

email address. I'm still adjusting in how to respond.

Alexios,

Did you set the set the seed to 1, because I'm looking at your results

and the numbers do not match with the numbers that I have provided.

I understand why the coefficients' estimates are similar but it doesn't

explain why other columns such as the t-value and pr are off by a large
margin. Also estimates for mu, ar*, ma*, omega, alpha1, and shape may have
large differences.

Take mu as an example:
-7.538187e+00 - (-7.877120e+00) = 0.338933, isn't that considered a

large difference to the point where it's safe to say that these two values
are not similar?

Another example is the t-values for x1 and x2:
x1 = 8.799994e+01   -  5.509361e+02 = -462.9362
x2 = 8.508606e+01   -  5.287634e+02 = -443.6773

An more alarming case that unfortunately I cannot share due to the data

being sensitive is that when the x variables' positions are switched, the
p-values are not the same. The p-value for a particular external regressor
went from 0 to 0.4385.

I will attempt to re-create a separate generic dataset that is similar

to the sensitive data that I am using.

Galib Khan


On Sun, Aug 19, 2018 at 10:06 PM, alexios galanos <alexios at 4dscape.com>

wrote:

I run the code you provided and obtain the following results related to

the external parameters:

Case 1 (x1,x2)
# x2 is second

            Estimate   Std. Error       t value  Pr(>|t|)
mxreg1  1.6724148 1.203377e-01  1.389767e+01 0.0000000
mxreg2  2.5310286 1.878833e-02  1.347128e+02 0.0000000

Case 2 (x2,x1)
# i.e. x2 is now first

mxreg1  2.5225382  0.04292725  58.7631024 0.000000e+00
mxreg2  1.6782986  0.12769622  13.1428990 0.000000e+00

Small differences in the coefficients are the result of the optimizer.

There may be an issues in the

way starting parameters are being generated based on some recent input

from Josh Ulrich (still to investigate)

and related to arima0 (used to generate start parameters), but otherwise

don?t see a large problem at first glance.

Alexios

On Aug 19, 2018, at 5:46 PM, GALIB KHAN <ghk18 at scarletmail.rutgers.edu>

wrote:

Recently I have discovered a problem with a package called rugarch that
creates arma-garch models. The issue is that if you literally change

the

positions of the x variables (external regressors) then you get two
completely different results.

In other words:

  - model1 = (arma(2,2) + garch(1,0) + x1 + x2)
  - model2 = (arma(2,2) + garch(1,0) + x2 + x1)
  - rugarch's output is essentially saying that model1 != model2
  - When the correct result should be model1 == model2

I may not know a lot of statistics but I know for a fact that if you

move

the x variables around, the output should still be the same.

Am I wrong on this?

Here's my stack exchange post that shows a generic R script proving my
point: Should the positioning of the external regressors change the

output

of arma-garch? (Possible rugarch bug/error)
<

https://stackoverflow.com/questions/51900177/should-the-positioning-of-the-external-regressors-change-the-output-of-arma-garc

Any feedback is welcomed.

Thanks

      [[alternative HTML version deleted]]

questions should go.

On Aug 19, 2018, at 9:02 PM, GALIB KHAN <ghk18 at scarletmail.rutgers.edu>

Sorry for sending this again, I didn't include r-sig-finance in the email

Alexios,

Did you set the set the seed to 1, because I'm looking at your results

I understand why the coefficients' estimates are similar but it doesn't

Take mu as an example:
-7.538187e+00 - (-7.877120e+00) = 0.338933, isn't that considered a large

Another example is the t-values for x1 and x2:
x1 = 8.799994e+01   -  5.509361e+02 = -462.9362
x2 = 8.508606e+01   -  5.287634e+02 = -443.6773

An more alarming case that unfortunately I cannot share due to the data

I will attempt to re-create a separate generic dataset that is similar to

Galib Khan


On Sun, Aug 19, 2018 at 10:06 PM, alexios galanos <alexios at 4dscape.com>

I run the code you provided and obtain the following results related to

Case 1 (x1,x2)
# x2 is second

            Estimate   Std. Error       t value  Pr(>|t|)
mxreg1  1.6724148 1.203377e-01  1.389767e+01 0.0000000
mxreg2  2.5310286 1.878833e-02  1.347128e+02 0.0000000

Case 2 (x2,x1)
# i.e. x2 is now first

mxreg1  2.5225382  0.04292725  58.7631024 0.000000e+00
mxreg2  1.6782986  0.12769622  13.1428990 0.000000e+00

Small differences in the coefficients are the result of the optimizer.

way starting parameters are being generated based on some recent input

and related to arima0 (used to generate start parameters), but otherwise

Alexios

On Aug 19, 2018, at 5:46 PM, GALIB KHAN <ghk18 at scarletmail.rutgers.edu>

Recently I have discovered a problem with a package called rugarch that
creates arma-garch models. The issue is that if you literally change the
positions of the x variables (external regressors) then you get two
completely different results.

In other words:

  - model1 = (arma(2,2) + garch(1,0) + x1 + x2)
  - model2 = (arma(2,2) + garch(1,0) + x2 + x1)
  - rugarch's output is essentially saying that model1 != model2
  - When the correct result should be model1 == model2

I may not know a lot of statistics but I know for a fact that if you

the x variables around, the output should still be the same.

Am I wrong on this?

Here's my stack exchange post that shows a generic R script proving my
point: Should the positioning of the external regressors change the

of arma-garch? (Possible rugarch bug/error)
<

Any feedback is welcomed.

Thanks

      [[alternative HTML version deleted]]